A chemometrics approach to analysis of NMR spectra can employ analysis techniques (e.g., partial least squares (PLS) regression) to relate a parameter (e.g., pH, density, concentration, boiling point and/or temperature) of a sample to NMR spectra. For example, a calibration model (e.g., calibration vector) can be used to analyze a NMR spectrum of a sample (e.g., using PLS) to determine a NMR prediction of the sample.
The calibration model and/or the calibration vector can be developed in a calibration stage. In the calibration stage, samples that are measured are typically of a known sample type (e.g., organic molecules, proteins and/or nuclei acids). Parameters of a multiple samples of the same sample type and/or corresponding NMR spectra of the multiple samples of the same sample type can be measured and/or analyzed (e.g., using PLS) to determine the calibration model and/or the calibration vector. The calibration model and/or the calibration vector can include values that can relate the measured parameters of the multiple samples to the corresponding measured NMR spectra.
NMR spectra are typically characterized by peaks at particular positions along the spectrum that are not influenced (or substantially not influenced) by environmental conditions (e.g., pH of a solvent that dissolves the sample, temperature of the solvent and/or measurement chamber of an NMR device, etc.). In some scenarios, the environmental conditions can cause the peaks to vary in position along the NMR spectrum. For example, a changing pH of the environment can cause the peaks to vary. Current methods of handling such cases can involve removing the peaks, whose position can vary depending on the environmental conditions and/or ignoring them in an analysis (e.g., PLS analysis of the NMR spectra). In these current methods, the remaining peaks (e.g., peaks that were not removed) can be analyzed with respect to their absolute values, ignoring the canceled-out peaks.
One disadvantage of these current methods can include ignoring important information that can be included in the ignored portions of the spectrum. Another disadvantage of these current methods can include restricting the analysis to use only absolute values of the remaining peaks, thereby, for example, making the analysis and/or the measurement results (e.g., determined parameters of a measured sample) sensitive to errors resulting from electronic parameters and/or other errors which are not canceled out by using relative values.